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Register a new userDeclaratively solving Google Code Jam problems with Picat
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Added by
Sergii Dymchenko
Publication date
2015
fields
Informatics Engineering
and research's language is
English
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In this paper we present several examples of solving algorithmic problems from the Google Code Jam programming contest with Picat programming language using declarative techniques: constraint logic programming and tabled logic programming. In some cases the use of Picat simplifies the implementation compared to conventional imperative programming languages, while in others it allows to directly convert the problem statement into an efficiently solvable declarative problem specification without inventing an imperative algorithm.
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In this paper we demonstrate several examples of solving challenging algorithmic problems from the Google Code Jam programming contest with the Prolog-based ECLiPSe system using declarative techniques like constraint logic programming and linear (integer) programming. These problems were designed to be solved by inventing clever algorithms and efficiently implementing them in a conventional imperative programming language, but we present relatively simple declarative programs in ECLiPSe that are fast enough to find answers within the time limit imposed by the contest rules. We claim that declarative programming with ECLiPSe is better suited for solving certain common kinds of programming problems offered in Google Code Jam than imperative programming. We show this by comparing the mental steps required to come up with both kinds of solutions.
Picat, a new member of the logic programming family, follows a different doctrine than Prolog in offering the core logic programming concepts: arrays and maps as built-in data types; implicit pattern matching with explicit unification and explicit non-determinism; functions for deterministic computations; and loops for convenient scripting and modeling purposes. Picat provides facilities for solving combinatorial search problems, including a common interface with CP, SAT, and MIP solvers, tabling for dynamic programming, and a module for planning. Picats planner module, which is implemented by the use of tabling, has produced surprising and encouraging results. Thanks to term-sharing and resource-bounded tabled search, Picat overwhelmingly outperforms the cutting-edge ASP and PDDL planners on the planning benchmarks used in recent ASP competitions.
Gecode is one of the most efficient libraries that can be used for constraint solving. However, using it requires dealing with C++ programming details. On the other hand several formats for representing constraint networks have been proposed. Among them, XCSP has been proposed as a format based on XML which allows us to represent constraints defined either extensionally or intensionally, permits global constraints and has been the standard format of the international competition of constraint satisfaction problems solvers. In this paper we present a plug-in for solving problems specified in XCSP by exploiting the Gecode solver. This is done by dynamically translating constraints into Gecode library calls, thus avoiding the need to interact with C++.
When creating a new domain-specific language (DSL) it is common to embed it as a part of a flexible host language, rather than creating it entirely from scratch. The semantics of an embedded DSL (EDSL) is either given directly as a set of functions (shallow embedding), or an AST is constructed that is later processed (deep embedding). Typically, the deep embedding is used when the EDSL specifies domain-specific optimizations (DSO) in a form of AST transformations. In this paper we show that deep embedding is not necessary to specify most optimizations. We define language semantics as action functions that are executed during parsing. These actions build incrementally a new, arbitrary complex program function. The EDSL designer is able to specify many aspects of the semantics as a runnable code, such as variable scoping rules, custom type checking, arbitrary control flow structures, or DSO. A sufficiently powerful staging mechanism helps assembling the code from different actions, as well as evaluate the semantics in arbitrarily many stages. In the end, we obtain code that is as efficient as one written by hand. We never create any object representation of the code. No external traversing algorithm is used to process the code. All program fragments are functions with their entire semantics embedded within the function bodies. This approach allows reusing the code between EDSL and the host language, as well as combining actions of many different EDSLs.
The CASH problem has been widely studied in the context of automated configurations of machine learning (ML) pipelines and various solvers and toolkits are available. However, CASH solvers do not directly handle black-box constraints such as fairness, robustness or other domain-specific custom constraints. We present our recent approach [Liu, et al., 2020] that leverages the ADMM optimization framework to decompose CASH into multiple small problems and demonstrate how ADMM facilitates incorporation of black-box constraints.
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